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Sergei Morozov

Personal Details

First Name:Sergei
Middle Name:
Last Name:Morozov
Suffix:
RePEc Short-ID:pmo411
[This author has chosen not to make the email address public]
http://www.wavelet3000.org

Affiliation

(in no particular order)

Morgan Stanley

http://www.morganstanley.com
New York

Department of Economics
Stanford University

Stanford, California (United States)
https://economics.stanford.edu/
RePEc:edi:destaus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
  2. Sergei Morozov, 2000. "Econometric Evaluation of Rational Belief Models," Econometric Society World Congress 2000 Contributed Papers 1654, Econometric Society.

Articles

  1. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.

    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    2. Michael C. Hatcher & Eric M. Scheffel, 2016. "Solving the Incomplete Markets Model in Parallel Using GPU Computing and the Krusell–Smith Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 569-591, December.
    3. Matt P. Dziubinski & Stefano Grassi, 2012. "Heterogeneous Computing in Economics: A Simplified Approach," CREATES Research Papers 2012-15, Department of Economics and Business Economics, Aarhus University.
    4. Yongyang Cai & Kenneth L. Judd & Greg Thain & Stephen J. Wright, 2013. "Solving Dynamic Programming Problems on a Computational Grid," NBER Working Papers 18714, National Bureau of Economic Research, Inc.
    5. Baştürk, N. & Grassi, S. & Hoogerheide, L. & van Dijk, H.K., 2016. "Parallelization experience with four canonical econometric models using ParMitISEM," Research Memorandum 013, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    7. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    8. John Gibson & James P Henson, 2016. "Getting the most from MATLAB: ditching canned routines and embracing coder," Economics Bulletin, AccessEcon, vol. 36(4), pages 2519-2525.
    9. Nalan Baştürk & Roberto Casarin & Francesco Ravazzolo & Herman K. Van Dijk, 2016. "Computational Complexity and Parallelization in Bayesian Econometric Analysis," Econometrics, MDPI, vol. 4(1), pages 1-3, February.
    10. Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.

Articles

  1. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.

    Cited by:

    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Davide Pettenuzzo & Francesco Ravazzolo, 2014. "Optimal portfolio choice under decision-based model combinations," Working Paper 2014/15, Norges Bank.
    3. Beechey, Meredith & Österholm, Pär, 2007. "The Rise and Fall of U.S. Inflation Persistence," Working Paper Series 2007:18, Uppsala University, Department of Economics.
    4. Miguel A.G. Belmonte & Gary Koop & Dimitris Korobilis, 2014. "Hierarchical Shrinkage in Time‐Varying Parameter Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 80-94, January.
    5. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    6. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    7. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
    8. Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99, Brandeis University, Department of Economics and International Business School.
    9. Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75, Brandeis University, Department of Economics and International Business School.
    10. Martin Feldkircher & Florian Huber, 2016. "Unconventional US Monetary Policy: New Tools, Same Channels?," Department of Economics Working Papers wuwp222, Vienna University of Economics and Business, Department of Economics.
    11. Kagraoka, Yusho & Moussa, Zakaria, 2013. "Quantitative easing, credibility and the time-varying dynamics of the term structure of interest rate in Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 25(C), pages 181-201.
    12. Marcel Fratzscher & Roland Straub, 2013. "Asset Prices, News Shocks, and the Trade Balance," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1211-1251, October.
    13. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," Working Papers 2017-026, Federal Reserve Bank of St. Louis.
    14. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    15. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    16. Frank Smets & Rafael Wouters, 2005. "Bayesian New Neoclassical Synthesis (NNS) Models: Modern Tools for Central Banks," Journal of the European Economic Association, MIT Press, vol. 3(2-3), pages 422-433, 04/05.
    17. Michal Franta & Jan Libich, 2024. "Holding the economy by the tail: analysis of short- and long-run macroeconomic risks," Empirical Economics, Springer, vol. 66(4), pages 1443-1489, April.
    18. Gary Koop & Lise Tole, 2013. "Forecasting the European carbon market," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, June.
    19. Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2010. "Changes in the transmission of monetary policy: evidence from a time-varying factor-augmented VAR," Bank of England working papers 401, Bank of England.
    20. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher & Florian Huber, 2019. "Spillovers from US monetary policy: evidence from a time varying parameter global vector auto‐regressive model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 831-861, June.
    21. Max Breitenlechner & Georgios Georgiadis & Ben Schumann, 2021. "What goes around comes around: How large are spillbacks from US monetary policy?," GRU Working Paper Series GRU_2021_003, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    22. Fratzscher, Marcel & Straub, Roland, 2010. "Asset Prices, News Shocks and the Current Account," CEPR Discussion Papers 8080, C.E.P.R. Discussion Papers.
    23. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    24. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    25. Arnoud Stevens & Joris Wauters, 2021. "Is euro area lowflation here to stay? Insights from a time‐varying parameter model with survey data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 566-586, August.
    26. Kateøina Šmídková, 2005. "How Inflation Targeters (Can) Deal with Uncertainty," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 55(7-8), pages 316-332, July.
    27. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2016. "Vulnerable growth," Staff Reports 794, Federal Reserve Bank of New York.
    28. Pär Österholm, 2006. "Incorporating judgement in fan charts," Finance and Economics Discussion Series 2006-39, Board of Governors of the Federal Reserve System (U.S.).
    29. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
    30. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    31. Miguel, Belmonte & Gary, Koop, 2013. "Model Switching and Model Averaging in Time- Varying Parameter Regression Models," SIRE Discussion Papers 2013-34, Scottish Institute for Research in Economics (SIRE).
    32. Luca Benati, 2004. "Evolving post-World War II UK economic performance," Bank of England working papers 232, Bank of England.
    33. Martin, Feldkircher & Thomas, Gruber & Florian, Huber, 2019. "International effects of a compression of euro area yield curves," Working Papers in Economics 2019-1, University of Salzburg.
    34. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial Conditions and Density Forecasts for US Output and Inflation," Working Papers 715, Queen Mary University of London, School of Economics and Finance.
    35. Rodríguez, Aldo, 2020. "Estimación Bayesiana de un Modelo de Economía Abierta con Sector Bancario," Dynare Working Papers 52, CEPREMAP.
    36. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
    37. Todd E. Clark & Fabian Krueger & Francesco Ravazzolo, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Papers (Old Series) 1439, Federal Reserve Bank of Cleveland.
    38. Yasutomo Murasawa, 2014. "Measuring the natural rates, gaps, and deviation cycles," Empirical Economics, Springer, vol. 47(2), pages 495-522, September.
    39. Luis Uzeda, 2018. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Staff Working Papers 18-14, Bank of Canada.
    40. Juan Manuel Julio, 2005. "Implementación, Uso e Interpretación del "Fan Chart"," Borradores de Economia 346, Banco de la Republica de Colombia.
    41. Hansen, Bruce E., 2006. "Interval forecasts and parameter uncertainty," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 377-398.
    42. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint Prediction Bands for Macroeconomic Risk Management," Working Papers No 5/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    43. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2020. "Modelling Returns in US Housing Prices – You’re the One for Me, Fat Tails," Working Papers 2020:13, Örebro University, School of Business.
    44. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.
    45. Giuseppe Pagano Giorgianni & Valeria Patella, 2024. "Belief distortions and Disagreement about Inflation," Working Paper series 24-08, Rimini Centre for Economic Analysis.
    46. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
    47. Frank Smets & Raf Wouters, 2004. "Forecasting with a Bayesian DSGE Model: an application to the euro area," Working Paper Research 60, National Bank of Belgium.
    48. Michal Franta & Jozef Barunik & Roman Horvath & Katerina Smidkova, 2011. "Are Bayesian Fan Charts Useful for Central Banks? Uncertainty, Forecasting, and Financial Stability Stress Tests," Working Papers 2011/10, Czech National Bank.
    49. Luca Benati, 2005. "U.K. Monetary Regimes and Macroeconomic Stylised Facts," Computing in Economics and Finance 2005 107, Society for Computational Economics.
    50. Koop, Gary & Korobilis, Dimitris, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2009-40, Scottish Institute for Research in Economics (SIRE).
    51. Dong Jin Lee, 2009. "Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process," Working papers 2009-26, University of Connecticut, Department of Economics.
    52. Beechey, Meredith, 2004. "Excess Sensitivity and Volatility of Long Interest Rates: The Role of Limited Information in Bond Markets," Working Paper Series 173, Sveriges Riksbank (Central Bank of Sweden).
    53. Bianchi, Francesco & Mumtaz, Haroon & Surico, Paolo, 2009. "The great moderation of the term structure of UK interest rates," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 856-871, September.
    54. Harrison, Richard & Taylor, Tim, 2012. "Non-rational expectations and the transmission mechanism," Bank of England working papers 448, Bank of England.
    55. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 869-889, August.
    56. Murasawa, Yasutomo, 2019. "Bayesian multivariate Beveridge--Nelson decomposition of I(1) and I(2) series with cointegration," MPRA Paper 91979, University Library of Munich, Germany.
    57. Francois R. Velde, 2004. "Poor hand or poor play? the rise and fall of inflation in the U.S," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 28(Q I), pages 34-51.
    58. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Research Working Paper RWP 11-16, Federal Reserve Bank of Kansas City.
    59. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    60. Crespo Cuaresma, Jesus & Doppelhofer, Gernot & Feldkircher, Martin & Huber, Florian, 2018. "Spillovers from US monetary policy: Evidence from a time-varying parameter GVAR model," Discussion Paper Series in Economics 31/2018, Norwegian School of Economics, Department of Economics.
    61. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    62. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    63. Thu, Le Ha & Leon-Gonzalez, Roberto, 2021. "Forecasting macroeconomic variables in emerging economies," Journal of Asian Economics, Elsevier, vol. 77(C).
    64. Rhys M. Bidder & Raffaella Giacomini & Andrew McKenna, 2016. "Stress Testing with Misspecified Models," Working Paper Series 2016-26, Federal Reserve Bank of San Francisco.
    65. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    66. Koop, Gary & Gefang, Deborah & Campolieti, Michele, 2012. "Time Variation in the Dynamics of Worker Flows: Evidence from the US and Canada," SIRE Discussion Papers 2012-69, Scottish Institute for Research in Economics (SIRE).
    67. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    68. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2008. "Inflation-Gap Persistence in the U.S," NBER Working Papers 13749, National Bureau of Economic Research, Inc.
    69. Benati, Luca, 2007. "The "Great Moderation" in the United Kingdom," Working Paper Series 769, European Central Bank.
    70. Ganics, Gergely & Mertens, Elmar & Clark, Todd E., 2023. "What Is the Predictive Value of SPF Point and Density Forecasts?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277622, Verein für Socialpolitik / German Economic Association.
    71. Wojciech Charemza & Carlos Díaz & Svetlana Makarova, 2015. "Ex-post Inflation Forecast Uncertainty and Skew Normal Distribution: ‘Back from the Future’ Approach," Discussion Papers in Economics 15/09, Division of Economics, School of Business, University of Leicester.
    72. Maximiano Pinheiro & Paulo Esteves, 2012. "On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information," Empirical Economics, Springer, vol. 42(3), pages 639-665, June.
    73. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    74. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2009. "On the evolution of the monetary policy transmission mechanism," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 997-1017, April.
    75. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    76. Colin Ellis & Haroon Mumtaz & Pawel Zabczyk, 2014. "What Lies Beneath? A Time‐varying FAVAR Model for the UK Transmission Mechanism," Economic Journal, Royal Economic Society, vol. 0(576), pages 668-699, May.
    77. Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2008. "On the Evolution of Monetary Policy," Working Paper series 24_08, Rimini Centre for Economic Analysis.
    78. Benati, Luca & Goodhart, Charles, 2007. "Investigating time-variation in the marginal predictive power of the yield spread," Working Paper Series 802, European Central Bank.
    79. Le Ha Thu & Roberto Leon-Gonzalez, 2021. "Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam," GRIPS Discussion Papers 21-03, National Graduate Institute for Policy Studies.
    80. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial indicators and density forecasts for US output and inflation," Temi di discussione (Economic working papers) 977, Bank of Italy, Economic Research and International Relations Area.
    81. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," LIDAM Discussion Papers CORE 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    82. Michal Franta & Jozef Baruník & Roman Horváth & Katerina Smídková, 2014. "Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests," International Journal of Central Banking, International Journal of Central Banking, vol. 10(1), pages 159-188, March.
    83. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    84. Lahiri, Kajal & Liu, Fushang, 2005. "ARCH models for multi-period forecast uncertainty-a reality check using a panel of density forecasts," MPRA Paper 21693, University Library of Munich, Germany.
    85. Koop, Gary & Tole, Lise, 2013. "Modeling the relationship between European carbon permits and certified emission reductions," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 166-181.
    86. Hassani, Hossein & Silva, Emmanuel Sirimal, 2018. "Forecasting UK consumer price inflation using inflation forecasts," Research in Economics, Elsevier, vol. 72(3), pages 367-378.
    87. Miles, William & Vijverberg, Chu-Ping, 2011. "Formal targets, central bank independence and inflation dynamics in the UK: A Markov-Switching approach," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 644-655.
    88. Andrew McKenna & Rhys Bidder, 2014. "Robust Stress Testing," 2014 Meeting Papers 853, Society for Economic Dynamics.
    89. Tim W. Cogley & Thomas J. Sargent, 2005. "Anticipated Utility and Rational Expectations as Approximations of Bayesian Decision Making," Working Papers 68, University of California, Davis, Department of Economics.
    90. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    91. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    92. Par Osterholm, 2008. "A structural Bayesian VAR for model-based fan charts," Applied Economics, Taylor & Francis Journals, vol. 40(12), pages 1557-1569.
    93. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    94. Mumtaz, Haroon, 2010. "Evolving UK macroeconomic dynamics: a time-varying factor augmented VAR," Bank of England working papers 386, Bank of England.
    95. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    96. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    97. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    98. Xun Zhang & Guihuan Zheng & Wei Shang & Shangying Xu & Xiaoguang Yang & Kin Keung Lai & Shou-Yang Wang, 2009. "An Integrated Decision Support Framework For Macroeconomic Policy Making Based On Early Warning Theories," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 335-359.
    99. Pervin, Shahida, 2018. "Dynamics and Interactions of Monetary Policy and Macroeconomic Variables: Empirical Investigation in the UK Economy with Bayesian VAR," MPRA Paper 91816, University Library of Munich, Germany.
    100. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    101. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Online Appendix to "Financial conditions and density forecasts for US output and inflation"," Online Appendices 14-103, Review of Economic Dynamics.
    102. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
    103. Katerina Smidkova, 2003. "Methods Available to Monetary Policy Makers to Deal with Uncertainty," Macroeconomics 0310002, University Library of Munich, Germany.
    104. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    105. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    106. Ciccarelli, Matteo & Darracq Pariès, Matthieu & Priftis, Romanos & Angelini, Elena & Bańbura, Marta & Bokan, Nikola & Fagan, Gabriel & Gumiel, José Emilio & Kornprobst, Antoine & Lalik, Magdalena & Mo, 2024. "ECB macroeconometric models for forecasting and policy analysis," Occasional Paper Series 344, European Central Bank.
    107. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (1) 2009-08-16

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